SGNMT is a tool for neural machine translation (NMT). It stands for Syntactically Guided Neural Machine Translation as it is designed to work well with the ucam-smt syntactical SMT system or other non-neural SMT systems. It builds up on the Blocks NMT example and adds support for n-best and lattice recoring, language models and much more. Currently, it supports

• Syntactically guided neural machine translation (NMT lattice rescoring)
• n-best list rescoring with NMT
• Ensemble NMT decoding
• Forced NMT decoding
• Integrating n-gram language models (srilm and nplm)
• Different search algorithms (beam, A*, depth first search, greedy...)
• Target sentence length modelling
• NMT training with options for reshuffling and fixing word embeddings
• ...

Please see the full SGNMT documentation for more information.

If you use SGNMT in your work, please cite the following paper:

Felix Stahlberg, Eva Hasler, Aurelien Waite, and Bill Byrne. Syntactically guided neural machine translation. In Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 16), August 2016. Berlin, Germany. arXiv